Replication Repository for�Press 1 for Roads�: Bridging Communication Gaps in Political RepresentationMiriam Golden, Saad Gulzar, Luke SonnetOverviewThis repository provides the data and code necessary to replicate the analyses in the paper �Press 1 for Roads.�The repository is structured into three main folders:* 1_data/: Raw and processed datasets* 2_analysis/: R scripts for data processing and analysis* 3_output/: Tables and figures produced by the analysisData Organization
The 1_data folder is organized into the following subfolders:1. 1_hh_data/ � Household survey data from baseline and endline surveys.2. 2_mpa_data/ � Data at the Member of Parliament level.3. 3_ps_data/ � Polling station�level data.4. 4_forecasting/ � Data from a forecasting exercise where respondents were asked to assess whether the intervention would be successful, based on information from the 2017 pilot.5. 5_2020_hujra_survey/ � Data from the 2020 hujra survey. Constituents reported on topics and issues discussed with MPs at their hujras (traditional home offices used by incumbents).6. 6_electoral_data/ � Election Commission of Pakistan data on the 2013 and 2018 general elections (processed for analysis).Analysis Scripts
The 2_analysis folder contains six R scripts:* 1_an_balance.R � Checks for balance and produces results reported in the paper.* 2_an_forecasting.R � Analyzes data from the forecasting exercise.* 3_an_hh.R � Analyzes household baseline and endline survey data.* 4_an_ps.R � Analyzes polling station data.
* helpers.R � Contains helper functions applied to household data; called by 03_an_hh.R and 04_an_ps.RAs of August 18, 2025, all code ran successfully on R version 4.4.1 (2025-06-14, "Race for Your Life").Outputs
The 3_output folder contains the replication results. It is organized into:* figures/ � All figures produced by the analysis scripts.* tables/ � All tables reported in the paper.Replication Workflow
The repository is designed so that users can replicate results by opening any of the R scripts in 2_analysis and running them. No manual editing of code is required once the data is downloaded and placed in the 1_data structure.Suggested workflow:
1. Check data and package setupo Ensure that all data folders under 1_data are present.o Open R (� 4.4.1) and install required packages if not already installed.2. Run scriptso Each script is self-contained. Running it will generate the corresponding outputs automatically in 3_output/figures and 3_output/tables.o Typical sequence if running all scripts:1. an_balance.R (balance checks)2. an_hh.R (household analysis)3. an_ps.R (polling station analysis)4. an_forecasting.R (forecasting exercise analysis)3. Review outputso Generated figures will be saved in 3_output/figures.o Generated tables will be saved in 3_output/tables.Notes* The repository is intended for replication and academic use only.* For questions, please contact the authors directly.* All constituency identifying codes in data have been randomly shuffled to maintain anonymity of treated locations. 
* To ensure numerical precision in calculations requiring consistent decimal points, some datasets are provided in RDS format. RDS files preserve R objects natively, preventing rounding or conversion errors that may occur with CSV files. Other datasets are stored as CSV files where such precision is less critical. This mixed approach ensures clean data submission while maintaining accuracy where necessary.